QUINT: A tool to detect qualitative treatment-subgroup interactions in randomized controlled trials

• Published in: Psychotherapy research 2016-09, Vol.26 (5), p.612-622
• Main Authors: Doove, Lisa L., Van Deun, Katrijn, Dusseldorp, Elise, Van Mechelen, Iven
• Format: Article
• Language: English
• Subjects: Adult; analisi di sottogruppi; Behandlungswirksamkeit; Clinical trials; Data Interpretation, Statistical; Drug use; efficacia del trattamento; Humans; interazione qualitativa; Motivational Interviewing - methods; Outcome Assessment (Health Care) - methods; qualitative interaction; qualitative Interaktion; QUINT; Randomized Controlled Trials as Topic - methods; subgroup analysis; Subgruppenanalyse; Substance-Related Disorders - therapy; treatment efficacy; 次團體分析; 處遇效能; 質性交互作用
• ISSN: 1050-3307; 1468-4381
• DOI: 10.1080/10503307.2015.1062934

Objective: The detection of subgroups involved in qualitative treatment-subgroup interactions (i.e., for one subgroup of clients treatment A outperforms treatment B, whereas for another the reverse holds true) is crucial for personalized health. In typical Randomized Controlled Trials (RCTs), the combination of a lack of a priori hypotheses and a large number of possible moderators leaves current methods insufficient to detect subgroups involved in such interactions. A recently developed method, QUalitative INteraction Trees (QUINT), offers a solution. However, the paper in which QUINT has been introduced is not easily accessible for non-methodologists. In this paper, we want to review the conceptual basis of QUINT in a nontechnical way, and illustrate its relevance for psychological applications. Method: We present a concise introduction into QUINT along with a summary of available evidence on its performance. Subsequently, we subject RCT data on the effect of motivational interviewing in a treatment for substance abuse disorders to a reanalysis with QUINT. As outcome variables, we focus on measures of retention and substance use. Results: A qualitative treatment-subgroup interaction is found for retention. By contrast, no qualitative interaction is detected for substance use. Conclusions: QUINT may lead to insightful and well-interpretable results with straightforward implications for personalized treatment assignment.